assets

a3ffd69a · Caroline DE POURTALES · 015c3256 · a3ffd69a · a3ffd69a · a3ffd69a
Commit a3ffd69a authored 2 years ago by Caroline DE POURTALES
--- a/assets/course_data_format.html
+++ b/assets/course_data_format.html
@@ -11,7 +11,7 @@
 <h2> An option to add information on model </h2>

 <h3> Why ? </h3>
-<p> There is a switch button, you can use it when you want to attach the csv you trained your model on or a feature mapping. This is useful when the values are categorical.
+<p> There is a switch button, you can use it when you want to attach the data (csv) you trained your model on or a feature mapping (txt). This is useful when the values are categorical.
 </p>

 <h3> How ? </h3>
@@ -22,11 +22,11 @@ If you decide to upload the feature mapping, it is a .txt file.

 <h2> What kind of model can I upload ? </h2>

-<p> You can only import .pkl models.</p>
+<p> You can only import .pkl models. But it works with joblib and pickle libraries. </p>

 <h2> What should the format of the instance be ? </h2>

-<p> You can either upload a .txt file containing hte instance with the format : feature1=value1,feature2=value2,... where feature1, feature2 are the names of the columns.
+<p> You can either upload a .txt file containing the instance with the format : feature1=value1,feature2=value2,... where feature1, feature2 are the names of the columns.

 But you can also upload a json of your instance.</p>


--- a/assets/course_decision_tree.html
+++ b/assets/course_decision_tree.html
@@ -2,13 +2,15 @@
 <html lang="en">
 <head>
    <meta charset="UTF-8">
-    <title>What kinf of model, data, or instance can I upload ?</title>
+    <title>Decision Tree</title>
 </head>
 <body>

-<h1> What library am I able to use on the platform ?</h1>
-
+<h2> What library am I able to use on the platform ?</h2>
 <p> Only models from scikit-learn are allowed.</p>

+<h2> Do I have to upload data ?</h2>
+You have to upload data or feature mapping when you have categorical data.
+
 </body>
 </html>
\ No newline at end of file
--- a/assets/course_random_forest.html
+++ b/assets/course_random_forest.html
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Random Forest</title>
+</head>
+<body>
+
+<h2> What library am I able to use on the platform ?</h2>
+<p> You can upload RandomForestClassifier from sklearn library or RF2001 models (from Aniti's team).</p>
+
+<h2> Do I have to upload data ?</h2>
+You need to upload the dataset your machine learning model trained on.
+
+</body>
+</html>
\ No newline at end of file
--- a/assets/typography.css
+++ b/assets/typography.css
@@ -2,6 +2,10 @@ body {
    font-family: sans-serif;
 }

+H1 {
+    font-family:Georgia, 'Times New Roman', Times, serif;
+}
+
 H4 {
    font-size: 20px;
    text-decoration-line:underline;

--- a/callbacks.py
+++ b/callbacks.py
@@ -29,12 +29,28 @@ def register_callbacks(app):
    page_list = ['home', 'course', 'application']

    # For home directory
-    page_home = dbc.Row([])
+    welcome_message = html.Div(html.Iframe(
+        src=app.get_asset_url("welcome.html"),
+        style={"height": "1067px", "width": "100%"},
+    ))
+    page_home = dbc.Row([welcome_message])
    # For course directory
+    course_data_format = html.Div(html.Iframe(
+        src=app.get_asset_url("course_data_format.html"),
+        style={"height": "1067px", "width": "100%"},
+    ))
+    course_decision_tree = html.Iframe(
+        src="assets/course_decision_tree.html",
+        style={"height": "1067px", "width": "100%"},
+    )
+    course_random_forest = html.Iframe(
+        src="assets/course_random_forest.html",
+        style={"height": "1067px", "width": "100%"},
+    )
    main_course = dcc.Tabs(children=[
-        dcc.Tab(label='Data format', children=[]),
-        dcc.Tab(label='Course Decision Tree', children=[]),
-        dcc.Tab(label='Course Random Forest', children=[])])
+        dcc.Tab(label='Data format', children=[course_data_format]),
+        dcc.Tab(label='Course Decision Tree', children=[course_decision_tree]),
+        dcc.Tab(label='Course Random Forest', children=[course_random_forest])])
    page_course = dbc.Row([main_course])

    # For the application